TY - GEN
T1 - Geo-referenced time-series summarization using κ-full trees
T2 - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
AU - Oliver, Dev
AU - Shekhar, Shashi
AU - Kang, James M.
AU - Laubscher, Renee
AU - Carlan, Veronica
AU - Evans, Michael R.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Given a set of regions with activity counts at each time instant (e.g., a listing of countries with number of mass protests or disease cases over time) and a spatial neighbor relation, geo-referenced time-series summarization (GTS) finds κ-full trees that maximize activity coverage. GTS has important potential societal applications such as understanding the spread of political unrest, disease, crimes, fires, pollutants, etc. However, GTS is computationally challenging because (1) there are a large number of subsets of κ-full trees due to the potential overlap of trees and (2) a region with no activity may be a part of a larger region with maximum activity coverage, making apriori-based pruning inapplicable. Previous approaches for spatio-temporal data mining detect anomalous or unusual areas and do not summarize activities. We propose a κ-full tree (κFT) approach for GTS which features an algorithmic refinement for partitioning regions that leads to computational savings without affecting result quality. Experimental results show that our algorithmic refinement substantially reduces the computational cost. We also present a case study that shows the output of our approach on Arab Spring data.
AB - Given a set of regions with activity counts at each time instant (e.g., a listing of countries with number of mass protests or disease cases over time) and a spatial neighbor relation, geo-referenced time-series summarization (GTS) finds κ-full trees that maximize activity coverage. GTS has important potential societal applications such as understanding the spread of political unrest, disease, crimes, fires, pollutants, etc. However, GTS is computationally challenging because (1) there are a large number of subsets of κ-full trees due to the potential overlap of trees and (2) a region with no activity may be a part of a larger region with maximum activity coverage, making apriori-based pruning inapplicable. Previous approaches for spatio-temporal data mining detect anomalous or unusual areas and do not summarize activities. We propose a κ-full tree (κFT) approach for GTS which features an algorithmic refinement for partitioning regions that leads to computational savings without affecting result quality. Experimental results show that our algorithmic refinement substantially reduces the computational cost. We also present a case study that shows the output of our approach on Arab Spring data.
KW - Full trees
KW - Georeferenced time-series
KW - Spatial data mining
KW - Summarization
UR - http://www.scopus.com/inward/record.url?scp=84873148340&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873148340&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2012.64
DO - 10.1109/ICDMW.2012.64
M3 - Conference contribution
AN - SCOPUS:84873148340
SN - 9780769549255
T3 - Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
SP - 797
EP - 804
BT - Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Y2 - 10 December 2012 through 10 December 2012
ER -